A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases

Physical exercise, obesity, inflammation and neutrophil extracellular traps (NETs): a review with bioinformatics analysis

Neutrophil extracellular traps (NETs) represent an innate organism defense mechanism characterized by neutrophil release of intracellular material to capture any aggressor agent. Elevated NETs release is associated with increased inflammatory response and related diseases, such as obesity. Chronic physical training is one of the main strategies to treat and prevent obesity. The relationship between physical training and NETs is still under study. The present review, followed by a bioinformatics analysis, demonstrates the meaningful connection between physical exercise, obesity, and NETs. The bioinformatics indicated TNF-α as a leading gene after the ontological analysis followed by positive-interleukin-6 regulation, chemokines, and inflammatory response regulation. The main results pointed to a relevant regulatory effect of physical training on NETs release, indicating physical exercise as a possible therapeutic target on modulating NETs and inflammation.

Integrated Analysis to Study the Relationship between Tumor-Associated Selenoproteins: Focus on Prostate Cancer

Selenoproteins are proteins that contain selenium within selenocysteine residues. To date, twenty-five mammalian selenoproteins have been identified; however, the functions of nearly half of these selenoproteins are unknown. Although alterations in selenoprotein expression and function have been suggested to play a role in cancer development and progression, few detailed studies have been carried out in this field. Network analyses and data mining of publicly available datasets on gene expression levels in different cancers, and the correlations with patient outcome, represent important tools to study the correlation between selenoproteins and other proteins present in the human interactome, and to determine whether altered selenoprotein expression is cancer type-specific, and/or correlated with cancer patient prognosis. Therefore, in the present study, we used bioinformatics approaches to (i) build up the network of interactions between twenty-five selenoproteins and identify the most inter-correlated proteins/genes, which are named HUB nodes; and (ii) analyze the correlation between selenoprotein gene expression and patient outcome in ten solid tumors. Then, considering the need to confirm by experimental approaches the correlations suggested by the bioinformatics analyses, we decided to evaluate the gene expression levels of the twenty-five selenoproteins and six HUB nodes in androgen receptor-positive (22RV1 and LNCaP) and androgen receptor-negative (DU145 and PC3) cell lines, compared to human nontransformed, and differentiated, prostate epithelial cells (EPN) by RT-qPCR analysis. This analysis confirmed that the combined evaluation of some selenoproteins and HUB nodes could have prognostic value and may improve patient outcome predictions.